Nikolaos Sifakis
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Nikolaos Sifakis has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Cristina Cristalli
Research for Innovation, AEA srl, Angeli di Rosora, 60030 Marche, Italy
1 shared publication - Daniela Isidori
Research for Innovation, AEA srl, Angeli di Rosora, 60030 Marche, Italy
1 shared publication - Dionysia Kolokotsa
Energy Management in the Built Environment Research Lab, Environmental Engineering School, Technical University of Crete, 73100 Chania, Greece
1 shared publication - Konstantinos Gobakis
European Dynamics SA, Fragoklissias 18 & Samou 53., Maroussi, 151 25 Athens, Greece
1 shared publication - Kostas Kalaitzakis
Department of Electronic and Computer Engineering, Technical University of Crete, GR-73100 Chania, Greece
1 shared publication - Nikolaos Kampelis
Energy Management in the Built Environment Research Lab, Environmental Engineering School, Technical University of Crete, 73100 Chania, Greece
1 shared publication
Latest Publications
- Open Access
HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response
Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met.
Open AccessHVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response
Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met.